Alternating Back-Propagation for Generator Network

Recovery error

We learn the model from the compressively sensed data (Can-dès, Romberg, and Tao, 2006). We generate a set of white noise images as random projections. We then project the training images on these white noise images. We can then learn the model from the random projections instead of the original images. We show the recovery error for different latent dimension d, where the recovery error is defined as the per pixel difference between the original image and the recovered image.